Effective Intrusion Detection in Highly Imbalanced IoT Networks with Lightweight S2CGAN-IDS

• URL: http://arxiv.org/abs/2306.03707v1
• Date: Tue, 6 Jun 2023 14:19:23 GMT
• Title: Effective Intrusion Detection in Highly Imbalanced IoT Networks with Lightweight S2CGAN-IDS
• Authors: Caihong Wang, Du Xu, Zonghang Li, Dusit Niyato
• Abstract summary: Internet of Things (IoT) networks contain benign traffic far more than abnormal traffic, with some rare attacks. Most existing studies have been focused on sacrificing the detection rate of the majority class in order to improve the detection rate of the minority class. We propose a lightweight framework named S2CGAN-IDS to expand the number of minority categories in both data space and feature space.
• Score: 48.353590166168686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Since the advent of the Internet of Things (IoT), exchanging vast amounts of information has increased the number of security threats in networks. As a result, intrusion detection based on deep learning (DL) has been developed to achieve high throughput and high precision. Unlike general deep learning-based scenarios, IoT networks contain benign traffic far more than abnormal traffic, with some rare attacks. However, most existing studies have been focused on sacrificing the detection rate of the majority class in order to improve the detection rate of the minority class in class-imbalanced IoT networks. Although this way can reduce the false negative rate of minority classes, it both wastes resources and reduces the credibility of the intrusion detection systems. To address this issue, we propose a lightweight framework named S2CGAN-IDS. The proposed framework leverages the distribution characteristics of network traffic to expand the number of minority categories in both data space and feature space, resulting in a substantial increase in the detection rate of minority categories while simultaneously ensuring the detection precision of majority categories. To reduce the impact of sparsity on the experiments, the CICIDS2017 numeric dataset is utilized to demonstrate the effectiveness of the proposed method. The experimental results indicate that our proposed approach outperforms the superior method in both Precision and Recall, particularly with a 10.2% improvement in the F1-score.

Related papers

• FedMSE: Federated learning for IoT network intrusion detection [0.0]
  The rise of IoT has expanded the cyber attack surface, making traditional centralized machine learning methods insufficient due to concerns about data availability, computational resources, transfer costs, and especially privacy preservation. A semi-supervised federated learning model was developed to overcome these issues, combining the Shrink Autoencoder and Centroid one-class classifier (SAE-CEN) This approach enhances the performance of intrusion detection by effectively representing normal network data and accurately identifying anomalies in the decentralized strategy.
  arXiv  Detail & Related papers  (2024-10-18T02:23:57Z)
• EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning [4.140068761522124]
  We propose an EG-ConMix method based on E-GraphSAGE, incorporating a data augmentation module to fix the problem of data imbalance. EG-ConMix exhibits significant advantages in terms of training speed and accuracy for large-scale graphs.
  arXiv  Detail & Related papers  (2024-03-24T04:09:48Z)
• Multi-class Network Intrusion Detection with Class Imbalance via LSTM & SMOTE [1.0591656257413806]
  This paper proposes to use oversampling techniques along with appropriate loss functions to handle class imbalance for the detection of various types of network intrusions. Our deep learning model employs LSTM with fully connected layers to perform multi-class classification of network attacks.
  arXiv  Detail & Related papers  (2023-10-03T07:28:04Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Machine Learning-Based Intrusion Detection: Feature Selection versus Feature Extraction [3.5889226512319903]
  Internet of things (IoT) devices are highly vulnerable to cyber-attacks. A variety of machine learning-based network intrusion detection methods for IoT networks have been developed. This paper provides a comprehensive comparison between these two feature reduction methods of intrusion detection in terms of various performance metrics.
  arXiv  Detail & Related papers  (2023-07-04T08:48:01Z)
• Uncertainty Estimation by Fisher Information-based Evidential Deep Learning [61.94125052118442]
  Uncertainty estimation is a key factor that makes deep learning reliable in practical applications. We propose a novel method, Fisher Information-based Evidential Deep Learning ($mathcalI$-EDL) In particular, we introduce Fisher Information Matrix (FIM) to measure the informativeness of evidence carried by each sample, according to which we can dynamically reweight the objective loss terms to make the network more focused on the representation learning of uncertain classes.
  arXiv  Detail & Related papers  (2023-03-03T16:12:59Z)
• Towards Reducing Labeling Cost in Deep Object Detection [61.010693873330446]
  We propose a unified framework for active learning, that considers both the uncertainty and the robustness of the detector. Our method is able to pseudo-label the very confident predictions, suppressing a potential distribution drift.
  arXiv  Detail & Related papers  (2021-06-22T16:53:09Z)
• Uncertainty-Aware Deep Calibrated Salient Object Detection [74.58153220370527]
  Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy. These methods overlook the gap between network accuracy and prediction confidence, known as the confidence uncalibration problem. We introduce an uncertaintyaware deep SOD network, and propose two strategies to prevent deep SOD networks from being overconfident.
  arXiv  Detail & Related papers  (2020-12-10T23:28:36Z)
• Lifelong Object Detection [28.608982224098565]
  We leverage the fact that new training classes arrive in a sequential manner and incrementally refine the model. We consider the representative object detector, Faster R-CNN, for both accurate and efficient prediction.
  arXiv  Detail & Related papers  (2020-09-02T15:08:51Z)
• BiDet: An Efficient Binarized Object Detector [96.19708396510894]
  We propose a binarized neural network learning method called BiDet for efficient object detection. Our BiDet fully utilizes the representational capacity of the binary neural networks for object detection by redundancy removal. Our method outperforms the state-of-the-art binary neural networks by a sizable margin.
  arXiv  Detail & Related papers  (2020-03-09T08:16:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.